Kubeflow: an end-to-end machine learning lab

 As was described at the begining of this chapter, there are many components of an end-to-end lab for machine learning reserch and development(Table 2.1), such as:

- A way to manage and version library dependencies, such as TensorFlow, and packge them for a reproducivle computing environment

- Interactive research environments where we can visualize data and experiment with different settings

- Provisioning of resources to run the modeling process in a distributed manager

- Robust mechanisms for snapshotting historical version of the research process

As we described earlier in this chapter, TensorFlow was designed to utilize distributed resources for training. To leverage this capability, we will use the Kubeflwo projects. Built on top of Kubeflow has several components that are useful in the end-to-end process of managing machine learning applications. To install Kubeflow, we need to have an exising Kubernetes control plane instance and use kubectl to launch Kubeflow's various components. The steps for setup differ slightly depending upon whether we are using a local instance or one of the major cloud providers.